For our experiment, we will explore DailyDialog dataset [1]}, a multi-turn dialogue dataset, which is pre-annotated with emotion and dialogue act labels.
Furthermore, the dataset will be augmented with politeness labels ranging from 1 to 5 by leveraging a pre-trained model [2]}.
By analyzing the correlation between politeness and emotional states, we discover that utterances with certain emotion classes are more polite or impolite than others.
At least frequently, it appears natural to use polite utterances in a happy emotional state, whereas to use impolite utterances in an angry state.
This phenomenon is precisely the motivation behind this experiment and we can find such utterance examples illustrated in Figure REF .
The conversation in this example shows how an impolite utterance is used in the Anger emotion state and then shifts with a polite utterance to the Happiness emotion state.
The results show this phenomenon statistically occurring significantly in the given dataset.
